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Recognition of the numbers is a necessary condition for the development of 3D street viewers. Using information about the position of the viewer relative to the detected site by self-driving car will make it possible to adjust the path and increase the accuracy of its implementation. The unmanned device (unmanned aerial vehicle or self-driving car) is equipped with a video camera or camcorder system. In the process of detecting house numbers, the image from the camera contains noises and redundant information. The preceding recognition step involves preliminary processing of the image obtained from the optical system. One of the techniques for removing noise from an incoming image is to perform an anti-aliasing operation. In this work we consider the filter presented by Peron and Malik, which is a modification of the heat equation. The choice of the method is due to the presence of a boundary detector, which allows us to save significant edges on the image when filtering noise. This is necessary, for example, for algorithms of recognition based on contour analysis. The recognition of filtered image is implemented in R with help of keras package. As a training set we use the Street View House Numbers (SVHN) Dataset, which is a realworld image dataset with minimal requirement on data preprocessing and formatting. It was shown that the primary filtering of test images allows us to increase accuracy of number recognition [ABSTRACT FROM AUTHOR]

Due to the massive development of information technology the amount of attacks on information systems has increased. Additionally, the attack features changed as well. They have acquired signs of hybridity, which is why it's much more difficult to prevent them. Information systems management is virtually impossible without security and safety management; whose main component is the assessment of existing risks. The subject of the study is the process of assessing the information security risk level that is being implemented through the fuzzy logic apparatus. The purpose of this work is to develop a methodology for assessing the information security risk degree, which would allow to eliminate the factor of uncertainty. The proposed methodology is to introduce term sets for each of the system characteristics and linguistic evaluation of the indicators. The article describes the main stages of implementation of the proposed methodology. The methodology presented in the article provides the opportunity to translate the obtained risk assessment results from a mathematical language into a linguistic form that is more comprehensible to the decision maker. This increases the effectiveness of the system security mechanisms. [ABSTRACT FROM AUTHOR]

In the work we consider the problem of optimal organization of healthcare system taking into account both map and statistical data. Map data include degrees of urbanization which classify the character of an area. This approach is based on mapping the territory by a grid square cell of 1 km2. Data about public health include research concerning spreading of basic infectious diseases and distribution of hospital system. Decision making is based on application of decision tree induction algorithm for tuples containing both numerical and categorized data. Map data were aggregated before they were used in learning tuples. Statistical data concerning public health were categorized with help of quantile classification method. We use a collection of functions of R software environment to visualize spatial urbanization data and models on top of static maps from various online sources. It includes tools common to those tasks, including functions for aggregation of spatial data and implementation of algorithm of decision tree induction in C5.0 package. [ABSTRACT FROM AUTHOR]